Noise-aware Client Selection for carbon-efficient Federated Learning via Gradient Norm Thresholding

Researchers propose a new client selection method for carbon-efficient federated learning that filters out noisy data to improve model performance. The approach uses gradient norm thresholding to better identify quality clients while maintaining sustainability goals in distributed AI training across renewable energy-powered data centers.